Transformer fault diagnosis based on new features selection and artificial bee colony optimization SVM

Abstract

In order to improve the accuracy of power transformers, a new DGA ratios based on support vector machine (SVM) and quantum-behaved particle swarm optimization with binary encoding (BQPSO) algorithm was proposed in the paper. First of all, 28 DGA ratios are used as the input vectors. Then, the SVM parameters and DGA ratios were simultaneously optimized by BQPSO. Finally, a transformer fault diagnosis model based on the artificial bee colony (ABC) algorithm optimization SVM combined with the cross validation (CV) principle is used to diagnostic fault. The diagnosis results show that the proposed DGA ratios increase the accuracy by 12%-28% over IEC ratios and DGA data. Furthermore, the accuracy of ABCSVM is better than the GASVM and standard SVM (accuracy rate are 84.35% 79.33%% and 65.41%).